






'''simple docstring'''





import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCAmelCase   (					A					:			int = 3		)    ->   qiskit.result.counts.Counts:
			if isinstance(A			,       A		):
						raise TypeError('''number of qubits must be a integer.'''		)
			if number_of_qubits <= 0:
						raise ValueError('''number of qubits must be > 0.'''		)
			if math.floor(A		) != number_of_qubits:
						raise ValueError('''number of qubits must be exact integer.'''		)
			if number_of_qubits > 1_0:
						raise ValueError('''number of qubits too large to simulate(>10).'''		)

			UpperCAmelCase_      :       Dict          =       QuantumRegister(A			,       '''qr'''		)
			UpperCAmelCase_      :       Union[str, Any]          =       ClassicalRegister(A			,       '''cr'''		)

			UpperCAmelCase_      :       Dict          =       QuantumCircuit(A			,       A		)

			UpperCAmelCase_      :       Any          =       number_of_qubits

			for i in range(A		):
						quantum_circuit.h(number_of_qubits - i - 1		)
						counter -= 1
						for j in range(A		):
									quantum_circuit.cp(np.pi / 2 ** (counter - j)			,       A			,       A		)

			for k in range(number_of_qubits // 2		):
						quantum_circuit.swap(A			,       number_of_qubits - k - 1		)

			# measure all the qubits
			quantum_circuit.measure(A			,       A		)
			# simulate with 10000 shots
			UpperCAmelCase_      :       List[Any]          =       Aer.get_backend('''qasm_simulator'''		)
			UpperCAmelCase_      :       List[str]          =       execute(A			,       A			,       shots=1_0_0_0_0		)

			return job.result().get_counts(A		)


if __name__ == "__main__":
						print(
						    f'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
						)










'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


